Method and system for obtaining a combination of faulty parts from a dispersed parts tree

ABSTRACT

It is an object of the present invention to find out parts to be a highly possible cause of failure without searching all of part data of all of products. Dispersed parts data on a parts tree are sequentially accessed from a set of known failed products, and part attribute values each having a higher support in the faulty product are extracted. In this process, a subset of parts used in the faulty product is also obtained simultaneously. The part attribute values having higher supports and the subset of parts used in the faulty product are represented as a tree in which a parts type serves as a node. Next, an information gain of a rule that having the two part attribute values is a cause of failure is calculated on two part attribute values having higher supports on the tree of the parts type. This calculation is locally performed on a common parent part of two parts and parts having a certain information gain is outputted as a cause of failure. How to select these two part attributes is performed in such a way that part attributes located closer to each other on the tree are first evaluated, and first found part attributes are made a candidate of a cause of failure.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is related to Japan Patent Application No. 2006-273514,filed Oct. 5, 2006 and is a continuation of U.S. patent application Ser.No. 11/865,199, filed Oct. 1, 2007.

FIELD OF THE INVENTION

The present invention relates to a method and a system for obtaining acombination of failed parts from dispersed parts tree. Morespecifically, the present invention relates to a method, system, andprogram product for obtaining a combination of parts characteristicscausing a failure from a set of dispersed parts tree and failedproducts.

BACKGROUND OF THE INVENTION

In a manufacturing industry such as an automobile industry wherecomplicated products are produced in large quantities, quality controlis one of the important issues. However, globalization and expansion incorporate activities lead to dispersion of databases used in the partsmanagement and the quality control, and thus global quality controlbeyond the fields is becoming difficult.

Meanwhile, a product trouble leading to a recall with a tremendous costis increasing due to complication of the product itself. In order toreduce such a cost, it is required to specify a cause of failure and totake necessary measures in an early stage of the failure becomingevident.

A method of controlling the quality in the manufacturing industrydepends on how to manage a development process and a manufacturingprocess, and data related thereto. For example, in the automobileindustry, data called a bill of materials is created during design andduring production to be utilized for executing the processes or solvingthe quality problems. As for a bill of materials used during designing(E-BOM; Engineering Bill Of Materials) and a bill of materials usedduring production (M-BOM; Manufacturing BOM), however, different billsof materials are usually used since the objects thereof are different.Similarly, even for a bill of materials regarding the same production,databases are separately owned by plants and parts suppliers and thereis no compatibility therebetween in many cases.

For example, since the bill of materials used during designing includesonly design data of the parts, what can be said is that a cause offailure may be the design of a specific part. When an impact of thefailure such as a recall is investigated from this bill of materials,all of the products produced according to its design will correspondthereto. Since the number of the impacted products are not able to benarrowed down, the recall is very expensive

Accordingly, if there are control data, such as parts data inproduction, for indicating that products of a certain lot (or a uniqueproduct serial number) use parts of a certain specified lot, it ispossible to say that, for example, only parts of a specific lot are thecause of the trouble. This is superior to the bill of materials duringdesigning as a quality control method since products using only thespecific lot may be a recall target.

However, since parts are produced in different plants, or come fromexternal suppliers, parts whose data may not be accessed, more detaileddata, such as the lot or the like, than the supplier's name may not beobtained. In order to perform the quality control, the informationinfrastructure should be improved in a certain way to thereby allow thedispersed quality control data to be systematically accessed and thedata to be traced.

The traces of such an individual product level and a parts lot level areeffective, but if there is a large amount of parts data in disperseddata sources, it is not realistic to access and trace all of the data.Particularly, when the trouble is caused not by a single part but by acombination of a plurality of parts, it is difficult to know whether ornot the combination is truly problematic by collecting only a lot numberof individual parts which can be traced from the known failed product.

SUMMARY OF THE INVENTION

The invention provides a method of obtaining a combination of partattribute values, as a candidate for cause of failure from data from abill of materials holding attribute values of the parts. The method mayinclude the general steps of: extracting part attribute values eachhaving a support higher than a predetermined value from a set of failedproducts; calculating an information gain of a rule that a combinationof the part attribute values each having the support higher than thepredetermined value is a cause of failure; and selecting the rule havingthe information gain larger than a predetermined threshold. The methodmay include having the part attribute values selected by regardingsequence values as a single value. The method step of extracting thepart attribute values each having the support higher than thepredetermined value, may include a part's subset of the parts used inthe failed product being simultaneously obtained. Further the methodstep of calculating the information gain, when pieces of partsinformation are dispersed at different locations, may include a set ofcommon parent parts of two parts is obtained by tracing from two partshaving respective attribute values, and an information gain of a rulethat having two part attribute values is a cause of failure iscalculated, from the subset of common parent parts used in the failedproduct, by using only sub-tree information of part of data of the billof materials. Additionally, the method step wherein a determination ofthe predetermined threshold is performed so that the number of possiblecombinations of part attribute values is narrowed down by graduallyincreasing the threshold.

The invention provides an information system for obtaining a combinationof part attribute values as a candidate of a cause of failure from dataof bill of materials holding attribute values of parts. The system maygenerally include: a part attribute value extracting section forextracting part attribute values each having a support higher than apredetermined value from a set of failed products; an information gaincalculating section for calculating an information gain of a rule that acombination of the part attribute values each having a support higherthan the predetermined value is a cause of failure; and an informationgain selecting section for selecting the rule having the informationgain larger than a predetermined threshold.

The invention also provides a computer program product for obtaining acombination of part attribute values as a candidate of a cause offailure from data of bill of materials holding attribute values ofparts. The computer program product generally causes a computer toexecute the steps of: extracting part attribute values each having asupport higher than a predetermined value from a set of failed products;calculating an information gain of a rule that a combination of the partattribute values each having the support higher than the predeterminedvalue is a cause of failure; and selecting the rule having theinformation gain larger than a predetermined threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a relation between data sources ofproducts and parts;

FIG. 2 is a diagram illustrating an example of a configuration of aninformation processing system which is one embodiment of the presentinvention;

FIG. 3 is a diagram diagrammatically illustrating a first step takingparts (an engine, a cylinder, a piston) of an automobile as an example;

FIG. 4 is a pattern diagram of an operation ofgetHighSupportAttributeSet( ) using the cylinder parts of the automobileas an example;

FIG. 5 is a diagram for explaining the calculation method of theinformation gain using only part of the parts tree with an example;

FIG. 6 is a diagram illustrating a implementation example of a failurediscovery, an analysis, a cause identification, and a recall targetprocess in an automobile manufacturing company;

FIG. 7 is a diagram illustrating an example of a GUI screen of thefailure discovery, the analysis, the cause identification, and therecall target process using the present invention in the automobilemanufacturing company;

FIG. 8 is a diagram for comparing a system performance of a conventionalmethod with that of the method of the present invention;

FIG. 9 is a diagram illustrating a case where the common parts C aremostly used in a faulty product and are all assembled from the parts Aand B having the same attributes;

FIG. 10 is a diagram illustrating a case where an information gain whichis locally high is provided, but there are many different common parentparts in reality, so that a correlation is not provided in somewhereelse; and

FIG. 11 is a diagram illustrating a hardware configuration of aninformation processing apparatus 1000.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, embodiments of the present invention will be described withreference to the drawings and formulas.

The technology of the aforementioned data mining or decision treeassumes that access to the data (a table, a transaction, or the like)can be easily and freely performed, and there is no technology premisingthat access to all of the products or all of the parts is impossiblesince the data sources thereof are dispersed.

For example, it is generally possible to find out a combination ofattribute values of the parts inducing a cause of failure, by employinga technique of the data mining for finding out a common item (anattribute value of the parts appeared in the failed product) from agiven data set (transaction). However, when an attempt is made tospecify a cause of failure by a technique using a confidence or the likewhich is commonly used, it is necessary to aggregate all of the partswith all of the attributes thereof into one table to access all of them,including not only the faulty product but also not faulty products. Thisis because that in order to calculate the value of confidence or lift orelse which is well known in the technique of the data mining, it isnecessary to also consider the parts data of sufficient not faultyproducts other than faulty products. Considering a situation where theparts data is dispersed in a plurality of plants or a plurality of partsuppliers, this is not clearly realistic. A calculation of aninformation gain used in a construction of the decision tree is alsosimilar to that.

FIG. 1 illustrates a relation between data sources of products andparts. Here, it is assumed that produced products (PD in the drawing)have data indicating that the products are constituted by what parts inthe dispersed data sources (DSs), and that parts constituting theproducts and their attribute values (L) such as a lot number thereof, orthe like can be searched once a product number is given. As for the databetween different data sources, however, all of the data can be obtainedby defining and tracing the relations therebetween. The relations arefundamentally unidirectional.

Here, when a quality problem causes product failure, the failure is notalways revealed on all the products, and instead a part of the faultyproducts can be identified. For example, product serial numbers offailed products are reported as faulty products. Among these qualityproblems, it is especially required to identify problems whose cause isa combination of parts belonging to different data sources (for example,different parts suppliers), by tracing part attribute data (for example,lot numbers of parts or the like) existing in the dispersed partdatabases from the known faulty products. Additionally, it is also anissue to predict a failure, which has not been revealed yet, byspecifying a cause of failure, to thereby search for a recall target ofthe products, for example.

Hence, it is one object of the present invention to specify, in a systemfor managing the product quality by dispersed parts tree data, whetheror not a combination of parts belonging to dispersed data sources (forexample, various parts suppliers) is a cause of failure by tracing partattribute data (for example, lot numbers of parts or the like) existingon a parts tree from known faulty products. Particularly, it is anobject to find out a highly possible cause of failure without performinga search of data of all parts of all products, whose access cost ishigh.

In one aspect of the present invention, there is provided a method forobtaining a combination of part attribute values as a candidate of acause of failure from data of bill of materials holding attribute valuesof parts, the method including the steps of: extracting part attributevalues each having a support higher than a predetermined value from aset of faulty products; calculating an information gain of a rule that acombination of the part attribute values each having the support higherthan the predetermined value is a cause of failure; and selecting therule having the information gain larger than a predetermined threshold.

Additionally, in the step of extracting the part attribute values eachhaving the support higher than the predetermined value, a subset of theparts used in the faulty products is simultaneously obtained. Further,in the step of calculating the information gain, when pieces of partsinformation are distributed at different locations, a set of commonparent parts of two parts is obtained by tracing from two parts havingrespective attribute values, and an information gain of a rule thathaving two part attribute values is a cause of failure is calculatedfrom the subset of common parent parts used in the faulty products, byusing only parts sub-tree information of the bill of materials, inaddition to the above-mentioned method.

This method can also be expressed as follows. The dispersed parts treeare sequentially traversed from a set of known faulty products (a set ofproducts reported to be failed currently) to access the parts data, anditems each having a higher support (part attribute values frequentlyappearing in the faulty products in common) in the faulty products areextracted. In this step, a subset of the parts used in the faultyproducts is simultaneously obtained for the respective parts having ahigher support. The part attribute values having a higher support andthe subset of parts used in the faulty products are represented as atree in which a parts type serves as a node. Incidentally, as for thepart attribute values, not only a single value, but also a discretesequence values and a ranged value are treated as one value.

Further, for two part attribute values having higher supports on theparts tree of the parts type, an information gain (Information Gain) ofa rule that having attribute values of the two parts is a cause offailure is calculated. This calculation is not performed by tracing allof the parts data of all of the products, but is locally performed todescendants of a common parent part of every pair of the two parts andoutputs pairs having a certain information gain as a cause of failure.Additionally, how to select these two part attributes is performed insuch a way that part attributes of the parts located closer to eachother on the parts tree are first evaluated, and first found partattributes are made a candidate of a cause of failure.

In the present invention, for example, when there is a database onfaulty products in which one record corresponds to one faulty productand which has attributes concerning various parts, and when an attributeand a value thereof are given, a ratio of the number of records havingthat attribute value to the number of all records is called the“support” for a failure of the product by taking that attribute value.Additionally, the “information gain” means a degree that one attributevalue of the product is statistically correlated with a product fault.Incidentally, for a general definition of the “support” and the“information gain,” technical books for data mining should be referredto.

The above-mentioned method can be implemented by a program causing acomputer to execute a function of each step, or an informationprocessing system in which the program is installed.

According to the present invention, it is possible to discover acombination of a plurality of parts dispersed to the data sources as acause of failure, which is difficult in a general data mining techniqueor a parts tracing from the viewpoint of performance. At this time, itis possible to calculate a combination having a higher information gain,from only local parts data, which can be accessed with relative ease,without tracing all of the data and parts. As a result, it becomespossible to narrow down target products to the necessary minimum in arecall process when a quality problem should occur, to make a root causeanalysis easier, or to predict the quality problem in advance to therebylead to an improvement in product quality or a reduction in developmentand maintenance cost.

It is difficult to obtain all of the parts used in products andattributes thereof, since data sources are dispersed. However, sincedata concerning one certain part is in one data source, it is possibleto access a large amount of data records for a single part with relativeease. Moreover, it is also easy to locally traverse a subtree of theparts, instead of the whole tree. It is supposed that this trace can notonly obtain descendant parts from products and parts, but also obtainall of ancestor parts (products) of parts having a specific attributevalue (a lot number), conversely.

Here, considering A as a total set of produced products, it is supposedthat a set F of products in which a failure is found in a sufficientlysmall size (for example, 100 units) compared with the number (forexample, 1 million units of produced products) of elements of set A(represented as PD_(p), for details, refer to definition of symbols usedin algorithm or problem definition described below) are identified, isgiven. It is possible to trace parts _(p)P_(ij) from each of the faultyproducts PD_(p) according to a parts tree. Here, i represent what layerthe part is in the parts tree (0 layer represents the product itself),and j represents what order the part is in the same layer. Additionally,the manner in which the part _(p)P_(ij) is assembled to the productPD_(p) or the parent part _(p)P_((i−1)j) is represented asPD_(p)→_(p)P_(ij) and _(p)P_((i−1)j)→_(p)P_(ij) respectively. Further,an attribute, such as a lot number, is attached to each part, and thisattribute value is represented with L_(t)(_(p)P_(ij)). A plurality ofattribute values may be defined to one part.

An object of the present invention is to look for a rule that theproduct PD_(p) has a fault when the attributes of two parts assembled tothe product PD_(p) simultaneously take certain values, respectively. Itis required that an information gain of the rule (Information Gain) IG() is sufficiently high. The information gain IG( ) is calculated from adecrease degree of entropy that the product fails or does not failbefore and after applying the rule. It is well known that the entropy iscalculated from formula −p log(p)−(1−p)log(1−p) where p is a probabilityof failing. Whether or not the information gain is sufficiently high canbe determined by defining an appropriate threshold of a differencebetween entropies. The threshold may be determined from systemengineering information of the product and statistical information onquality, or may also be experientially determined by executing thepresent algorithm.

FIG. 2 illustrates an example of a configuration of an informationprocessing system which is one embodiment of the present invention. Inthis system, a product manufacturing plant system 10, first-tiersupplier systems (20 and 30), and a second-tier supplier system 40 areconnected to each other through a network 50. The drawing shows up tothe second-tier supplier for simplification, and also many suppliersystems over a third-tier supplier, a fourth supplier, or the like canbe included. Hereinafter, each of these systems is also called a datasource.

Each data source is constituted by, a CPU (11 a, 21 a, 31 a, 41 a) as acontrol unit, a storage unit (11 b, 21 b, 31 b, 41 b; also includingdatabases on a memory and a hard disk), a network controller (11 c, 21c, 31 c, 41 c), and the like, and these data sources are connected toeach other through the network 50, such as the Internet or the like. Thesame network or the different networks may be used as the network 50between the data sources. One same network controller may be used as thenetwork controllers (11 c, 21 c, 31 c, 41 c) since they can beconstituted in common to the same network, or three or more networkcontrollers may be used if three or more different networks areconnected. A portion of parts data (small circle) is stored in eachdatabase, and a configuration of the parts is represented with a treestructure. A child-parent relationship of the tree is represented withthe dashed-line arrow. The root part of the data source is representedas a virtual node (small circle of the dashed line) in a leaf node ofthe data source which manages the parent part. Although the leaf nodeand the virtual node are arranged in the different data sources, theyhave the same relation logically and are connected with the arrow of thedashed line. Each CPU can access the memory and the database in the samedata source freely at a low cost to perform the processing, but when itaccesses another data source, communication on the network is required,so that there are problems that the amount of data to be processed islimited and the access takes time comparatively. Incidentally, the arrowof the solid line represents the access to the data and a call ofcontrol upon proceeding the process.

The data source 10 in which the product is used as the root also has auser interface controller 12, and first receives a list of the productsin which a quality problem has occurred as an input. The CPU 11 aexecutes an algorithm mentioned below, but when it is necessary toprocess the parts data stored in the memory other than its own memory,it transmits a request, including a required processing, informationabout a parts ID, or the like through the network 50, to ask the CPU ofthe data source in which the parts data exists to process it. Theprocessing result is sent from a requested CPU to a requesting CPU uponcompleting the process, and the requesting CPU continues the process.Upon completing all of the processes, the data source 10 of the productnotifies a user of the processing result through the user interfacecontroller 12 and completes the process.

A system level procedure in FIG. 2 will be described. A productionhistory that by what kind of parts all of the produced products areassembled is stored. Logically, the parts assembled to a certain productor part is represented with the tree structure as shown by the dashedline in the drawing, and a manufacturing feature that the part has,namely, an attribute value, such as a lot number, manufacturing means, alocation, time, an operator(s), or the like is stored in each noderepresenting the part. Physically, not all of the information on theparts is stored in one database, but the information on the parts isstored by the plant and the supplier which manufacture the parts. Whenanother supplier's part is searched (traced), since the supplier'snetwork information (address on the network) and the parts ID arerecorded on the corresponding location of its own hard disk, it is readout therefrom, and an information acquisition is requested to thecorresponding supplier through the network. The supplier who hasreceived the request reads the corresponding information from its ownhard disk based on the parts ID, and returns the result to an originalproduct manufacturing plant (OEM) and a higher level parts supplier bythe communication on the network. Also when tracing a logical treeranging over a plurality of nodes, the information acquisition isperformed by repeating such a procedure in a manner similar to that.Further, it is also possible to trace toward the root of the tree(namely, product). It is possible to execute this in a similar procedureto that if the similar network information and parts ID are stored alsofor a parent's nodes (parts).

Hereinafter, the procedure will be described using elements on thissystem. This system will be described by separating into three sections,namely, (1) part attribute value extracting section, (2) informationgain calculating section, and (3) information gain selecting section,according to the function.

(1) <Part Attribute Value Extracting Section>

It gets a set of faulty product IDs, traces parts used in respectivefaulty products on the above-mentioned system, and searches for partshaving attribute values which appear frequently. Details thereof will bedescribed in the following (a) through (c).

(a) The set of faulty product IDs is passed as an input to the productmanufacturing plant system 10. Since the product manufacturing plantsystem 10 has information that from what parts the product of each ID isassembled in its hard disk, but does not have attribute information ofthe parts themselves therein, it sends a calculation request to thefirst-tier supplier systems 20 and 30 of the respective parts throughthe network communication using the set of parts IDs as a parameter asdescribed in the above-mentioned system.

(b) The first-tier supplier system 20 which has received the calculationrequest receives the set of parts IDs used in the faulty products, andcalculates whether or not there is any attribute value which appearsfrequently among the attribute values of the parts. It is possible tocalculate this by searching the parts database stored in the hard diskwith the parts ID and counting each attribute value to thereby obtain asupport. The first-tier supplier system 20 returns the parts each havinga support higher than a predetermined threshold and attribute valuesthereof to the product manufacturing plant system 10. The productmanufacturing plant system 10 records the received parts and theattribute values thereof as the part attribute which appear frequently.

(c) When the parts of the second-tier supplier system 40 in a lowerlevel are used in producing the supplier's parts, the calculationrequest is further sent to the second-tier supplier system 40 by usingthe set of IDs of the lower parts as a parameter in a manner similar tothat of (a). The second-tier supplier system 40 calculates a partattribute value which appears frequently in a manner similar to that of(b), and notifies the answer to the product manufacturing plant system10. This will be repeated until the check for all of the parts iscompleted.

(2) <Information Gain Calculating Section>

Regarding a combination of arbitrary two parts having the attributevalues appearing frequently, which are obtained in above mentioned step(1), an information gain as to whether or not a rule that a failure iscaused by the combination is statistically meaningful is calculated. Ifthe higher information gain is selected, the combination of two partattributes considered to be a cause of failure is determined. Detailsthereof will be described in the following (a) through (d).

(a) A combination of certain two parts is considered. A common partassembling those parts is obtained. Information on an assembly relationbetween these parts is grasped at the product manufacturing plant, sothat it is possible to search them.

(b) The calculation request is sent to the supplier of the common partby the network communication using types of two parts and respectiveattribute values appearing frequently as a parameter.

(c) The supplier of the common part receives the calculation request of(b), and calculates the set of IDs of the common parts using both of twoparts having the two attribute values. A calculation method thereof isperformed by obtaining a set which uses the respective parts having theattribute values appearing frequently and calculating the setintersection by either method among the following methods.

(i) If both of the received parts are directly assembled to the commonpart, searching the attribute values thereof makes it possible todirectly calculate a set of common parts IDs corresponding thereto. Atthis time, if it is necessary to inquire the lower level supplier aboutthe required attribute, it is performed by transmitting a searchcondition (to have the attribute value appearing frequently) through thenetwork, performing the corresponding search in the lower levelsupplier, and returning the result.

(ii) If both of the received parts or either of them is not the directlyassembling part, it is necessary to perform a reverse trace from thepart to the common part. First, a supplier who manufactures the part andholds the attribution information thereof is obtained. This may beinquired to the product manufacturing plant system 10, and may beinquired in turn to the lower level supplier systems. Next, theattribute value appearing frequently is passed to the supplier system,and communication for requesting the start of the process is performed.The supplier system receives the request to calculate the set of partsIDs having the attribute values thereof. Next, the reverse trace to thecommon part is performed for every element of the set.

(d) The supplier of the common part can calculate the information gainfrom a set of IDs of the common part using both of the parts appearingfrequently, and a set of common parts used in the faulty product whichhas already been obtained in the above step (1). This can be calculatedby a difference between an entropy of failure in all of the commonparts, and an entropy of failure in the common part using the partshaving the attribute values appearing frequently (this can be obtainedby a product of the two sets). A calculation method of the entropy isthe same as that given by a typical definition of the amount ofinformation.

(3) <Information Gain Selecting Section>

(e) If the information gain calculated in step (d) of the above step (2)is larger than a predetermined threshold, the parts having the twoattribute values may be a cause of failure, so that the result is sentto the product manufacturing plant system 10.

(f) The product manufacturing plant system 10 then repeats thereceptions (a) (b) and (e) of the above step (2) for all of thecombinations, and then can acquire two parts possibly be a cause offailure and the combination of the attribute values thereof by summarizethe received answer.

[Definition of Symbol Used in Algorithm and Problem Definition]

Hereinafter, more details of the algorithm cooperatedly processed byeach CPU will be described. Symbols used in the present specificationand the drawings will be defined as follows. In the definition, acharacter with underline shall represent a set.

PD_(s)≡product with serial number s

A≡set of all of the products

F≡set of faulty products. In the present invention, it shall be given.

_(p)P_(ij)≡j-the part in i-th layer assembled to product PD_(p).Specifically, _(p)P_(0j) is the same as PD_(p).

_(p)P_(ij)→_(p)P_((i+1)k)≡_(p)P_((i+1)k) is a k-th child of _(p)P_(ij)in the part tree. Particularly, the part assembled to the product PD_(p)is written as PD_(p)→*→_(p)P_(ij).

L_(t)(_(p)P_(ij))≡value of attribute t of part _(p)P_(ij)

FIS _(s) ≡Frequent item set (Frequent Item Set) {L_(t)( )=L, L_(u)( )=M,. . . }. Set of attributes (L_(t)( ),L_(u)( ), or the like) which appearfrequently in the faulty products in a subset S of the product, and setof values thereof. Here, L, M, or the like is the attribute value, andincludes not only a single value but sequence values and a ranged value.In the following algorithm, FIS is represented by the tree and the partattribute, such as L_(t)=L, and the value thereof are added to the node.

sup_(s)(L_(t)( )=T)≡|{PD_(p)∈S|∃i∃j(L_(t)(_(p)P_(ij))∈T)}|/|S|. Notethat, T is in the value range of L_(t)( ) and is S⊂A.

P _(ij)≡set of parts P_(ij)

P _(ij|C)≡set of parts P_(ij) which satisfies condition C

P _(ij|) _(F) ≡set of parts P_(ij) assembled to set of faulty product F.Particularly, P _(O┐|) _(F) =F

H(F, S)≡entropy which fails in product subset S

H(F, P _(rq″|Lt( )=LΛLu( )=M″))=−p log(p)−(1−p)log(1−p). Note that,p=|{P_(rq)|P_(rq)→*→P_(ij) andL_(t)(P_(ij))=LΛP_(rq)→*→P_(kl)ΛL_(u)(P_(kl))=M}∩{P_(rq)|PD_(p)∈FΛPD_(p)→*→_(p)P_(rq)}|/|{P_(rq)|P_(rq)→*→P_(ij)ΛL_(t)(P_(ij))=LΛP_(rq)→*→P_(kl)ΛL_(u)(P_(kl))=M}|.

H(F, P _(rq))=−q log(q)−(1−q)log(1−q).

Note that, q=|{P_(rq)|PD_(p)□FΛPD_(p)→*→_(p)P_(rq)}|/|P_(rq)|

IG(F, S1 →S2 )≡information gain; H(F, S1 )−H(F, S2 ) IG(F, P _(rq)→P_(rq|″Lt( )=LΛLu( )=M″)=H(F, P) _(rq))−H (F, P _(rq″Lt( )=LΛLu( )=M″))

In the present invention, the problem is solved roughly by two steps.

At a first step (it is called a FIS (Frequent Item Set) building step),the dispersed parts data are sequentially accessed according to theparts tree only from a limited set of faulty products (set of productsreported to be failed in early stage), and items having higher supports(part attribute values appearing in the failure in common) in the faultyproduct are first extracted. In this process, the used parts are tracedfrom each faulty product, and the set of parts used in the faultyproduct is also searched for simultaneously. The part attributes havinghigher supports and the set of parts used in the faulty product arerepresented as the parts tree. This parts tree is represented as FIS.

At the second step, the information gain of a rule that the product isfailed when two part attribute values are selected and both of thevalues are taken is calculated from FIS (rules derivation step; it iscalled the rules derivation step). At this time, the data of all of theproducts are not traced but a common parent part of two parts issearched for, and a set of parts used in the faulty product among theparent parts, a set of parts using the part which takes two partattribute values, and a total set of parts are searched for, so that theentropies before and after the rule is applied are calculated from theseand the information gain is calculated from the difference therebetween.

Details of the basic algorithm of the first step (FIS building step) areas follows. First, a failure cause parts tree FIS to be the result isinitialized (FIS is considered as a set and is taken as empty set) (1).Next, the part tree of the product is sequentially traced from the root(product itself) (2, 3). The node corresponding to FIS is added first(4). A set of corresponding parts (P _(ij|) _(F) ) traced from the givenset of faulty s s products (F) is searched for to attach the set to FIS.In practice, since P _(ij|) _(F) is sequentially searched for from theroot of the tree, it is possible to search for P _(ij|) _(F) only bytracing by one level from the parts of the set in the parent node whichis higher by one level (5). Here, if there is no part attribute value,such as the lot number, in the part P_(ij), the process will be moved tothe following part. If there is the part, it is considered to be afunction from the part to the attribute value, and it is representedwith L( ) (6). Here, a higher support (a ratio that the attribute of thefailed product is its value) in the failed product (F) is selected byusing a subprocedure getHighSupportAttributeSet( ) which creates andreturns a linked attribute value to attach it to the node of FIS (7).

FIG. 3 diagrammatically illustrates the first step taking a part (anengine, a cylinder, a piston) of an automobile as an example. Symbol 60is a set of reported disabled cars (here, there are only two cars of car1 and car 2 for simplification), and symbols 70, 80, and 90 are P _(ij|)_(F) of respective parts, in which lot 1, lot A, and the like have thepart attribute values (they are lot numbers of the cylinder and thepiston, respectively) having higher support. The set of faulty parts andthe attribute having higher support searched for in the way are attachedto a failure cause part tree 106, and the first step is completed if allof the parts have been checked. The built failure cause parts tree 106is an output of the first step.

Describing more specifically, an engine supplier 20 receives informationon an engine 1 and an engine 2 which are used in the disabled car from avehicle maker 10. While two cylinders are used in each engine, three outof four cylinders are used as the lot 1, so the support of the lot 1 ishigh as 0.75, and thus it is included in a candidate, but the support ofthe lot 2 is 0.25, so it is not included in the candidate. Similarly, apiston supplier 30 receives information that the cylinders used in thedisabled car are cylinders 11, 12, 21, and 22 from the engine supplier20 in a higher level, and a support of the piston of the lot A is 0.5among these cylinders, so it is included in the candidate, whereassupports of the lot B and the lot C are low at 0.25, respectively, sothey are not included in the candidate. Since the engine supplier 20 hassearched for the cylinder of the lot 1 as a characteristic of the partwhich appears frequently in the disabled car, it transmits theinformation to the vehicle maker 10. Similarly, the piston supplier alsotransmits information that the piston of the lot A is frequently used tothe vehicle maker 10.

[Subprocedure getHighSupportAttributeSet( )]

The set of parts (P _(ij|) _(F) ) used in the faulty products, itsattribute (L), and the minimum support (min-sup) are given, and a set ofvalues of the parts included in the set and of attribute values havingsupports not less than the minimum support is returned. Note hereinthat, if a connected set (a set of serial lot numbers or the like) ofattribute values satisfies the conditions, such a connected set willalso be included in one of the values.

A result set V is initialized (1), and a set of attribute values of theparts which the faulty products have is set to T (2). The followingprocess is performed for every subset of T in descendent order (3).First, a not connected subsets of T are removed (4). At this time,subsets being meaningless as the attribute value are also furtherremoved using heurisitics rules or the like. While regarding the overallsubset as a single attribute value, a support thereof (in a case of aset, all subsets of T including the subset are counted as items) iscalculated. A support is a ratio of the number of products including atleast one part which takes its attribute value among the sets F offaulty products. If the support is not less than the given min-sup, theattribute value thereof is added to V (5). After all of the connectedsets have been checked, V is returned (6).

FIG. 4 is a pattern diagram of an operation ofgetHighSupportAttributeSet( ) using the cylinder part of the automobileas an example. Here, sup (Lot1) in the drawing indicates a support ofLot1. In this drawing, while there are Lot1, Lot2, Lot3, and the like asthe lot number of the cylinder, it is indicated here that Lot1 and{Lot2, Lot3} satisfy min-sup=1, where Lot2 and Lot3 are continuousvalues.

Incidentally, the min-sup whish is specified upon callinggetHighSupportAttributeSet( ) from a main procedure of the first stepcan be calculated from a predicted failure rate (ε<<1) of the part.Namely, it is assumed that there are parts which do not take thatattribute value by the failure rate (1−ε).

Details of the rules derivation step (Rule Derivation step) which is thesecond step will be described hereinafter. First, a common minimumparent node of two parts (a node nearest from two parts among commonparent nodes) is searched for. Next, a set of parts having two partattribute values is searched for in respective parts, and a set of partsof the common minimum parent nodes, which uses both of these two partsis searched for by a trace. An entropy that the product fails bysimultaneously taking these two attribute values is calculated from aintersection of the set of parts using both of the two parts which aresearched for here, and the set of parts used in the faulty productswhich has been searched for at the first step, on the part of the commonminimum parent node. Further, it is possible to search for an entropybefore the rule is applied from the total set of parts of the commonminimum parent nodes and the set of parts used in the faulty productswhich has been searched for at the first step. The information gain canbe calculated from the difference of these entropies. An informationgain which is higher than a certain value is outputted as a candidate ofa cause of failure.

(1) Result rule set (O) which becomes a result of the present inventionis initialized with the empty set. From following (2) or later, the rulewhich satisfies the conditions of the result (a combination of lotnumbers of a plurality of parts which cause a failure or the like) isadded.

(2) It is a start of a loop for checking all of the combinations of theattribute values of the parts which satisfy a given minimum support onthe failure cause tree searched for at the FIS building step, fromfollowing (3) or later. Lot numbers L and M of parts called a part A anda part B shall be paid attention from (3) or later.

(3) A minimum common part (a common parts located closer to each otheron the parts tree) among the common parts assembling two parts A and thepart B which are currently seen is searched for. It is possible tosearch for this by using a database often used in a product managementsystem called E-BOM (bill of materials of design level). This commonpart is set to C. (4) An information gain that the failure is causedwhen the lot numbers of the part A and the part B are L and M,respectively, is calculated from the set of parts used in the faultyproducts, which have already been searched for at the FIS building stepor parts using the lot L and the lot M, by the data on the part C.

The calculation of this information gain will be described in moredetail. Since the total number of the part C and the number of the partC used in the faulty products (this set has already been searched for atthe FIS building step and attached to the failure cause tree) are known,the entropy whether or not the product fails on the parts of the part Cis represented with following equation.

Supposing that

P=(the number of the part C used in the faulty products)/(the totalnumber of the part C)

it is possible to calculate the entropy by using −P log P−(1−P)log(1−P).This is well known as a calculation method of the entropy of the amountof information.

Next, in order to calculate the information gain, the all parts C whichthe parts A having the lot number L and the parts B having the lotnumber M are both assembled to is searched for by a trace, and a similarentropy is calculated within the set of parts.

Namely, supposing that Q=(the number of the parts C, which use both ofthe lot L of the part A and the lot M of the part B and are also used inthe faulty products)/(the number of the parts C using both of the lot Lof the part A and the lot M of the part B),

it is similarly calculated by using −1−Q log(1−Q)−Q log Q. Thedifference between these entropies is the information gain. Thisindicates how much the combination of the lot numbers of the part A andthe part B contributes to the product fault.

(5) If this information gain is larger than a determined value, the lotnumbers L and M of the part A and the part B are added to the set ofresults.

(6) It moves to the next cycle of the loop for the next combination.

(7) The set of results is checked and the threshold of the informationgain used in (5) is adjusted. Namely, the threshold will be reduced ifthere are insufficient results, and, on the contrary, the threshold willbe increased if there are too many results, and then it will repeat from(1) or later.

FIG. 5 is a diagram for explaining the calculation method of theinformation gain using only part of parts tree with an example. Theparts considered from (2) or later is sets 131 and 132 of the lotnumbers of the lot A and the lot B of the part A and the part B,respectively. Incidentally, the sets of lot A and lot B used in thefaulty products which has been searched for at the FIS building step arerepresented by lines 122 and 123, for reference. Ratios of the sets 122and 123 to the sets 131 and 132 indicate the respective supports, andthese satisfy a condition of not less than the minimum support.

Here, the part C to which the part A and the part B are assembled incommon is found (a set 130 in the center of FIG. 5). While such a commonpart in a level higher than the part C is possibly found, a minimum part(a part nearest to the part A and the part B on the part tree) amongthem shall be selected.

Next, parts which are the parts of the part C and uses the parts of thelot A of the part A and the parts of lot B of the part B is searched forby a part tracing function. In FIG. 5, the arrow indicates an assemblyrelation. This arrow is traced conversely. Generally, such a tracerequires a higher cost, but since the part C, and the part A and thepart B are located closer to each other on the parts tree, the cost isrelatively low even if a remote access is required. The set indicated bythe central symbol 131 is the set of searched parts of the part C(leveled as Caf). Note herein that the set of parts (Cf) used in thefaulty products of the part C has already been searched for at the FISbuilding step.

Here, it is possible to calculate the aimed information gain from all ofthe parts C, the Cf subset 121, and the Cab subset 130. Incidentally,such a calculation can be strictly performed in the set of products (atthe left corner in FIG. 5) by tracing the whole part tree, but since thedata sources are dispersed as described in the problem of the presentinvention, a trace on the tree from any part to the assembling productis difficult. Hence, according to the present invention, the trace ofthe part A, the part B, and the part C is performed within onlyneighboring data sources whose access cost is relatively low, thereby itis possible to calculate the information gain.

In one embodiment FIG. 6 is an implementation example of a failurediscovery, an analysis, a cause identification, and a recall targetprocess in an automobile manufacturing company.

In a process 1 of FIG. 6, an example of quality problems (abnormaloperation, generation of unusual sound, or the like) are reported from arepair plant, a manufacturing site, or the like. In a process 2, a knownset of disabled cars is searched for from the reported cases where thefailure possibly occurs due to a specific cause using existingtechniques, such as text mining or the like from the reports. It canassume that two or more independent causes of failure are not mixedclearly in a failed product set F of the present invention. A process 3is a process of searching for a combination of part attributes to be thecause of failure according to the present invention.

More specifically, in the process 3, a quality engineer perform anoperation using GUI as shown in FIG. 7. First, a “carVIEW” node on ascreen is clicked, and the set of disabled cars (specifically productserial number) searched for in the process 2 is inputted. Since theparts of the automobile are displayed on the parts tree, a node of asuspicious part is clicked based on experience and knowledge of theengineer to display the attribute of the part, or to graphically displaythe support. If the support of the attribute value of the part used inthe faulty product is high, the attribute is clicked and marked. This isrepeated to other parts, and thereafter, the second step of the presentinvention is executed to automatically display the combination of theparts having higher information gain on the screen. At this time, theinformation gain is graphically displayed, and the most suspiciouscombination is determined as a cause of failure. If there is nosuspicious combination, the same procedure is then repeated for thesingle part to determine a suspicious single part. At this time, it ispossible to identify a more accurate cause of failure using visualizedinformation, such as the information gain or the experience andknowledge of the engineer (process 4).

In a process 5, the result of the process 4 is used, and an automobilethat may fail is determined by using quality control data, such as billof materials. In a process 6, a recall process, such as recall andrepair, is executed only for the necessary minimum vehicle.

In a second embodiment, the combination of the parts which becomes acause of failure can be found according to processes similar to those (1through 4) of the first embodiment. An engineer of a product developmentexecutes a root cause analysis based on such information, thereby it ispossible to identify a fundamental problem, for example, inconsistencyof engineering parameters (a size, an output, or the like) of two parts)on engineering more readily, and take required actions (a redesign, achange of a production method, or the like) quickly.

Further, such an analysis makes it possible to predict an occurrence ofnew quality problems in advance and to suppress the cost of the productdevelopment by taking an appropriate preliminary action.

(1) A case where faulty products whose number is so sufficient that thesize of the faulty product set is statistically meaningful about a causeof failure is effective (it is shown that when there is only one failedproduct due to one cause, (for example, when only one product ismanufactured from a part of a certain lot to be a cause of failure) orwhen all of the manufactured products may be failed (for example, designproblem), the present invention is inapplicable).

(2) Further, a case where these causes (part to be the cause) are notobvious, and a physical analysis is difficult by obtaining actual failedproducts is effective. For example, the cases where the recall from theused is difficult since cooperation of the user is not obtained theproduct is so physically huge that the movement is difficult, sending anengineer or the analysis in the field is difficult, the failed productsitself are lost, or the like.

(3) The production history data for every part are dispersed. Thosetraces take time and effort.

(4) The cause of failure can be assumed to be the same one throughanalysis on data in the quality reports, such as a symptom of thefailure. Namely, the set of faulty products in which two or moreindependent causes are mixed is not given. For example, it is possibleto analyze symptoms, such as “an unusual sound is heard from an engine”,“a wiper does not work”, “a door does not open”, or the like to therebynarrow down to only a single symptom (it can be not always narrowed downto one at 100%).

(5) The attribute (lot number or the like) of each part intended to be atarget here does not have a correlation between the attribute values ofthe parts which are different from each other. If not, for example, ifall of the parts of a product produced in April have a single lotnumber, respectively, it is not possible to give the answer based on thepresent invention even if a failure occurs for the product produced inApril. It is because the supports of the lot numbers of all of the partsbecome high as they take a single value, and all of the respectiveinformation gains become the same. Since it is unnatural that the lotnumbers of all of the parts take the same value equally, this assumptionis sufficiently realistic.

(6) The cost of the reverse trace from the ID of the part which has theattribute value appearing frequently in the faulty products to theproduct is high, and the trace is not so accurate.

(a) Since the bill of materials is usually used also for assembly work,it includes detailed information that which sub-part is assembled towhich part. In many cases, since mapping of 1 to 1 is achieved, and anindex and a pointer are fully equipped, the trace in forward directioncan be performed easily and highly precisely. However, information wherea certain part is used is required in the reverse direction, but suchinformation is not directly stored in the database in a form, such as areverse pointer, so that it is necessary to search the database (it isexpensive compared with the index or the pointer). Particularly, thatthe part of the lower level supplier is used in which part of the higherlevel supplier is not identified by the lower level supplier, and it isnecessary to request the higher level supplier to perform the similarsearch through the network, so that the communication cost is furtheradded.

(b) Further, the reverse trace is not configured in the form of 1 to 1mapping in many cases from a viewpoint of manageability of holding data,economical efficiency, or the like. For example, there is a case whereit can be traced only by a many-to-many relation when seeing it as asingle piece of part, such that the part of this lot is assembled bysub-parts of this lot and that lot. In this case, not only it isexpensive, but also an accuracy of the result due to the reverse trace(it is desired to search for a product using this part, but even aproduct which does not actually use it will be included, or the like) isdecreased. There is a demerit in this that, in addition to an increasein calculation amount due to expansion, an accuracy of a calculationresult also gets worse.

(1) A discovery example of a cause of failure resulting from amanufacturing problem or a difference in supplier lots: a failure whichhappens due to the combination of two parts resulting from amanufacturing variation. For example, an unexpected wear occurs due tomismatching between diameter sizes of a piston part and a cylinder partof an engine, resulting in an unusual vibration of the engine. Thediameters of the piston and the cylinder are decided by a supplier (whenthere are a plurality of suppliers), a lot number, a manufacturingoperator, a manufacturing machine, or the like. Particularly, since thelot number given a value correlated with other attributes in many cases,it turns out that a problem is caused in a specific combination of thepiston and the cylinder by applying the present invention using the lotnumber as the attribute.

(2) A discovery example of a defect cause due to a design change of bothor either parts in the combination of two parts: similarly, mismatchingbetween the piston and the cylinder of the engine. This time, supposingthat the design of the piston is changed and a minor number of a partnumber is increased by one. However, an interference with a cylinder ofa specific lot occurs due to the design change, results in a breakage inthe worst case. In this case, if the part number is also considered asthe attribute, a combination can be found by using the lot number of thecylinder and the part number of the piston as the attributes. Noteherein, as for a problem of a new product design itself, since failuresshould occur in all of the manufactured products, it is not included inthe explanation of the present specification.

Hereinafter, a trial calculation is made about improvement in systemperformance of the present invention. Here, following preconditions areset.

(Precondition of Calculation in Automotive Manufacturing Industry)

100,000 cars per year are produced in a certain type of car (it is about300 cars per day), the part management of a finished car is for about 1million cars as ten years on the average.

The number of parts used in one finished car is hundreds of thousands,but the number of major parts which need to be managed in the databaseis 50,000.

Supposing that the number of parts directly treated by the OEM (productmanufacturing plant) among the above-mentioned major parts is hundreds.A part hierarchy is up to three through five layers, and supposing thatchild parts of about 10 to 100 are assembled to one part. If modeling ismade such that a first layer is assembled by the parts of 100, secondand third layers are assembled by the parts of 10, and a fourth layer isassembled by the parts of 5 on average in the part hierarchy of fourlayers, one finished car will be made from the major parts of about50,000 as represented by following equation.

1*100*10*10*5

Supposing that each supplier treats about 1 to 10 parts, and there areabout 50 first-tier suppliers, and 3 to 4 second-tier suppliers andthird-tier suppliers for parent parts, respectively, resulting in50*3*3, so that there are about 500 suppliers, and respective assembledparts and detailed information thereof shall be owned by each supplier.However, the OEM or the company of the parent part shall grasp a Serialnumber or a lot number of the parts which they use for the assembly.

the OEM and respective suppliers (500 companies) are connected with eachother through the Internet, and exchange not only a regular trade butalso quality information on the part or the like by a Web base systemcalled a supplier portal. An information exchange is implemented as aWeb application by using a protocol of not only HTTP but Web service.Since the communication of the Internet is T1 (1.5 Mbps) or E1 (2 Mbps),it is assumed a transmission speed thereof is about 256 Kbytes/sec.

As an attribute of one part, there are a manufacturing date, a worker, adescription of activity, an assembly part, an operation history, or thelike other than a parts number, a lot number, and a serial number, and arecord length per part shall be about 500 bytes.

First, when a normal OEM solves the same problem as that of the presentinvention, it seems to be natural to uses apriori (and an improvedalgorithm thereof) which is well known for the data mining. In thiscase, it is necessary to be able to access all of the part data from aprocessing computer of the OEM. Even when the most efficient apriorialgorithm is used, all of the records are accessed at least once. Sincethe number of parts is 50,000 and the record length is 500 bytes perpart, a database size becomes so large that 1 million cars×50,000parts×500 bytes=tens of terabytes. Since original data are distributedto 500 suppliers, accessing them in each case makes the communication tobe tens of terabytes, so that it becomes impossible. (even when itbecomes tens of bytes supposing that only required data are sent among500 bytes, it takes tens to millions of seconds=several weeks). Inpractice, supposing that only production information on one day is sentjust once per day as a batch it will be 300 cars×50,000 parts×500byte=7.5 G bytes and will be completed about 30 minutes, but even insuch a case, it is necessary to access all of the databases of tens ofterabytes Even when there is a high-speed secondary storage device (forexample, transfer rate of 100 Mbytes to 1 Gbyte per second), it will betens of thousands to hundreds of thousands of seconds=several hours toseveral days, for the access of tens of terabytes, so that it cannot beadmitted from the object of finding a fault at the earliest possibledate. Moreover, a cost of maintaining tens of terabytes of databases isalso high.

Next, not mining the database which is centrally controlled in onelocation, but sequentially accessing each distributed database tocalculate a confidence, an information gain, or the like is considered.In order to confirm effectiveness of a calculation (hereinafter,referred to as a sub-tree calculation) using a part of information(sub-tree) on the bill of materials, which is one of the solving meansof the present invention, a trial calculation of a case where thesub-tree calculation is not used is made first, here.

First, a lot of faulty parts, which appear frequently (having a largesupport), are calculated. Cars reported having a fault are assumed to be1000 in number, here, and the product serial number of the part withabout 20 bytes per car is sent to each supplier. Additionally, supposingthat there are 100 primary parts per car. The communication from the OEM(product manufacturing plant) to the first-tier suppliers is1000×20×100=2 Mbytes, so it is the communication within 1 second. It isa search of about millions of parts data (as a database size, it ishundreds of Mbytes) at each supplier, but since a support of a specificlot including 1000 cars (several Mbytes) which are search targets isonly calculated, the search and the calculation time are considered tobe about several seconds. Next, the product serial numbers of the partsused in 1000 cars of the faulty products are similarly sent to thesecond-tier suppliers, 1000×20×10=0.2 Mbytes (Here, supposing that thereare ten secondary parts in one primary part. Additionally, when notingthat the processes of 100 first-tier suppliers are performed inparallel, what is necessary is to consider only the time of one firstsupplier substantiality), and it will be several seconds even when asimilar processing time in the second-tier suppliers is added thereto.The third-tier suppliers and the fourth-tier suppliers may also besimilar to that. The communication for reporting the lot of the parthaving higher support to the OEM has no problem in time. Consideringabove, the time while all of the lot numbers of the parts having highersupport are collected in the OEM is considered to be within about 10minutes.

Next, the information gain (it may be a confidence) of the combinationof the parts having higher support is calculated. The combination of thelot numbers of the two different parts of the fourth suppliers shall beconsidered. Additionally, the number of parts per one lot is about tensto hundreds of parts, and since there is a tendency that the number ofparts per lot is increased in a lower hierarchy in particular, anaverage of a lot size of the first-tier to fourth-tier parts is set to10, 10, 100, and 1000 in number, respectively.

Supposing that the sub-tree calculation of the present invention is nottaken into consideration here, the reverse trace to the product level ofthe OEM will be performed. In the case of the product used in the lot ofthe fourth part, 1000×100×10×10=10 million, so it will be not less than10 million cars. In practice, since there is a part which is used inneither the corresponding product nor the parent part thereof, it willbe a number that is little more limited. Supposing that the parts ofabout 1/10 to ½ thereof are used, it will become 100×50×5×5=125,000,namely, hundreds of thousands of part data. The amount of data will behundreds of Mbytes even when each part information is tens of bytes, andconsidering the transfer rate, it will take about 10 to 20 minutes (notethat, in the case of reverse trace, since the number of suppliers ofeach layer is fundamentally one, neither the communication nor thecalculation can be performed in parallel).

Further, in the OEM, the data of hundreds thousands of cars will beaccessed from the database for 1 million cars, but this is considered totake time since there are many hit counts for the search (Although it isnot so many as the total access, it will become tens of % of the totalnumber of records, and the amount of data transfer itself becomeshundreds of Mbytes). Supposing that the calculation in the OEM takesabout 30 minutes, the calculation is completed in about 1 hour percombination of the parts having higher support even when the sub-treecalculation of the present invention is not taken into consideration, sothat it turns out that the calculation is completed within several hourseven when there are a plurality of combinations. Namely, the calculationwhich takes several days to several weeks can be completed in aboutseveral hours, so that it turns out that a significant improvement inefficiency can be achieved by using traceability on the distributeddatabase even when the sub-tree calculation is not taken intoconsideration.

Lastly, a trial calculation on an improvement in efficiency due to alocal calculation of the sub-tree calculation of the present inventionwill be made. The procedure of searching for the lot of the part havinghigher support in the beginning is the same (time for about 10 minutes).

Next, a combination of two parts of the fourth-tier suppliers iscalculated. The common supplier may be the third-tier supplier. It isbecause it is appropriate to consider that the combination of two partscorrelated with the failure is physically and logically near rather thanbeing completely far on the tree.

If the combined parts and the lot numbers thereof are found, thethird-tier supplier does not need to receive the data from thecorresponding fourth-tier supplier. Namely, if the information on thecombination of the lot numbers of the fourth-tier parts is received fromthe OEM, the set of parts using the lots can be calculated from only thelocal database of its own. Since the lot size of the fourth-tier part isassumed to be 1000, the third-tier part data of about 1000 to 2000 willbe searched from the 1 million part databases. This and the data of thethird-tier parts used in the faulty products, which is traced in theforward direction trace from the faulty product have the amount of dataof about 1000 (when two or more are used in one product, they are notless than 2000), so that the calculation of these product sets is alsoperformed without problems. Generally, it may be considered that thedatabase search and the calculation of this level take about severalminutes. Here, if the information gain of the calculation result islarger than the predetermined threshold, the combination of the lotnumbers of the parts will be returned to the OEM as a candidate of acause of failure.

When noting that each combination can be calculated in parallel by thedifferent third-tier suppliers, if the sub-tree calculation of thepresent invention is used, the response can be returned in about 10 to15 minutes even when how many combinations there are (except a casewhere two combinations are calculated by the same supplier). Theabove-mentioned discussion will be summarized as shown in FIG. 8. FIG. 8is a diagram for comparing a system performance of a conventional method(apriori) with that of the method of the present invention.

Incidentally, referring to an economic effect, since it takes severaldays for the result to come out according to the typical method usingthe apriori, 300 cars are produced per day and shipped to a market, whenthese 300 cars may become the recall target, if a recall cost per car isestimated at 100,000 yen, a cost, such as 30 million yen per day, 100million yen for 3 days, and 300 million yen for 10 days will beadditionally required.

Moreover, the effect of the sub-tree calculation can shorten thecalculation time of 4 to 5 hours to about 10 minutes. In this case, itwill not become a large amount of money in converting into the recallcost, whereas in a case of a fault having high danger involving humanlives, if an accident should occur, it leads to not only a compensationfor damage but also an immeasurable damage against a credibility and animage of the company or the like, so that it is impossible to convert itinto the amount of money. Moreover, insurance does not cover the damageagainst the company reliability, either. In this case, it is clear thata solution as quick as 1 hour or 1 minute is desirable.

Moreover, when considering that the present invention is not used onlywhen a special case where the product causes an accident, but is used anapplication of calculating metrics, such as a supplier's qualityevaluation or the like on a daily basis (tracing from a low qualityproduct which is not failed to identify the supplier), it is verymeaningful that the calculation time is shortened from several hours toabout 10 minutes in terms of an improvement in efficiency of IT.

In order to calculate the information gain that the combination ofcertain parts fails, to calculate it by reverse-tracing to the productlevel and to locally calculate it on the level of the intermediatecommon parent part are not the same. Here, it is discussed that there isnot any problem in this calculation method practically.

First, while a result that there is a meaningful correlation whencalculating the information gain in the product level comes out, suchresult may not be obtained when locally calculating it. For example, asshown in FIG. 9, a case where certain parts C are mostly used in thefaulty product and are all assembled from the part A and B having thesame attribute is considered.

In this case, since most of the common parent parts are used in thefailed product, naturally the combination of the part A and the part Bwhich are the assembly parts thereof also occupy most of the commonparent parts C. For that reason, since the original entropy in thecommon parent part C is extremely low (most of them fail), theinformation gain is hardly obtained. However, as can be seen from FIG.9, the combination of the lot a of the part A and the lot b of the partB clearly contributes to failing.

Note herein, it is clear that the common parent part C becomes a causeof failure by itself in this case, and it is immediately calculated thatC has a large correlation with the failure. However, whether or not thepart C, the part A, the part B, or the combination thereof is the causecannot actually be determined from the method, such as the data mining.Generally, it is necessary to use an investigation by the engineer, ananalysis including other products, or the like.

Next, contrary to the above-mentioned case, there is a case where theinformation gain which is locally high is provided, but there are manydifferent common parent parts in reality, so that the correlation is notprovided in somewhere else as shown in FIG. 10. In this case, when it iscalculated by reverse-tracing to the product level, the information gainwill not become so high.

In FIG. 10, when only the common parent part C is seen, it is shown thatthe correlation for the combination of the lot a of the part A and thelot b of the part B to fail is high within C. However, the same lot ofthe same part is used also in another common parent part C′, and whenthe information gain is calculated within C′, the entropy may converselyincrease.

In such a case, there is a possibility of resulting in an improperconclusion unless all of the common parent parts are checked. As onesolution, it is possible to solve it in such a way that the informationgain is calculated on all of the common parent parts, and theinformation gain increases in all of them, (in a rule that there is acontribution only when the information gain in all of the parent partsis high, when at least one information gain does not increase, therewill be no contribution), or a majority thereof is taken.

FIG. 11 illustrates a hardware configuration in which each data sourceof the information processing system shown in FIG. 2 is represented asan information processing apparatus 1000. Hereinafter, an overallconfiguration will be described as the information processing apparatustypically indicating a computer, but it is needless to say that anecessary minimum configuration can be selected according to theenvironment.

The information processing apparatus 1000 is provided with a CPU(Central Processing Unit) 1010, a bus line 1005, a communication I/F1040, a main memory 1050, a BIOS (Basic Input Output System) 1060, aparallel port 1080, a USB port 1090, a graphics controller 1020, a VRAM1024, a voice processor 1030, an I/O controller 1070, and input means,such as a keyboard and mouse adapter 1100. Memory means, such as aflexible disk (FD) drive 1072, a hard disk 1074, an optical disk drive1076, a semiconductor memory 1078, or the like can be connected to theI/O controller 1070.

An amplifier circuit 1032 and a loudspeaker 1034 are connected to thevoice processor 1030. Additionally, a display unit 1022 is connected tothe graphics controller 1020.

The BIOS 1060 stores a boot program product that the CPU 1010 executesupon booting the information processing apparatus 1000, a programproduct depending on the hardware of the information processingapparatus 1000, or the like. The FD (flexible disk) drive 1072 reads theprogram product or the data from a flexible disk 1071, and provides itfor the main memory 1050 or the hard disk 1074 through the I/Ocontroller 1070.

As the optical disk drive 1076, a DVD-ROM drive, a CD-ROM drive, aDVD-RAM drive, and a CD-RAM drive can be used, for example. In thiscase, it is necessary to use an optical disk 1077 corresponding to eachdrive. The optical disk drive 1076 reads the program product or the datafrom the optical disk 1077, can also provide it for the main memory 1050or the hard disk 1074 through the I/O controller 1070.

The computer program product provided to the information processingapparatus 1000 is stored in a record media, such as the flexible disk1071, the optical disk 1077, or memory card, and is provided by theuser. This computer program product is read from the recording mediumthrough the I/O controller 1070, or is downloaded through thecommunication I/F 1040, thereby it is installed in the informationprocessing apparatus 1000 to be executed. Since the operation that thecomputer program product causes the information processing apparatus toperform is the same as that in the apparatus which has already beendescribed, it will be omitted.

The above-mentioned computer program product may be stored in anexternal storage medium. As the storage medium, an optical magneticrecording medium such as MD, and a tape medium other than the flexibledisk 1071, the optical disk 1077, or the memory card can be used.Additionally, a storage unit, such as a hard disk or an optical disklibrary provided in a server system connected to a privatetelecommunication line or the Internet may be used as the recordingmedium to provide the computer program product to the informationprocessing apparatus 1000 through a communication line.

The above example mainly described on the information processingapparatus 1000, but the program product having the function described onthe information processing apparatus is installed in the computer tooperate the computer as the information processing apparatus, therebymaking it possible to achieve a similar function to that of theinformation processing apparatus described above. For that reason, theinformation processing apparatus described as one embodiment in thepresent invention can also be achieved by the method and the computerprogram product thereof.

The apparatus of the present invention can be achieved as a hardware, asoftware, or a combination of the hardware and the software. In animplementation by the combination of the hardware and the software, atypical example includes an implementation by the computer system havinga predetermined program product. In this case, the predetermined programproduct is loaded in and executed by the computer system, thereby theprogram product causes the computer system to perform the processingaccording to the present invention. This program product is constitutedby a group of instructions which can be expressed by an arbitrarylanguage, a code, or a notation. Such an instruction group enables thesystem to perform a specific function directly or after either or bothof (1) conversion to another type of language, code, or notation, and(2) duplication to another medium are performed. Of course, the presentinvention encompasses not only such a program product itself but also aprogram product including a medium with the program product recordedthereon. The program product for performing the function of the presentinvention can be stored in an arbitrary computer readable medium, suchas a flexible disk, an MO, a CD-ROM, a DVD, a hard disk drive, a ROM, anMRAM, a RAM, or the like. In order to store in the computer readablemedium, the program product can be downloaded from a different computersystem connected via a communication line or can be reproduced fromanother medium. The program can also be compressed or divided intoplurality to be stored in a single or multiple recording medium.

As mentioned above, although the present invention has been describedbased on the embodiments, the present invention is not limited to theembodiments described above. Furthermore, the advantages of theembodiments of the present invention are described only by way of citingthe most suitable advantages derived from the present invention, andthus the advantages of the present invention are not limited to thosedescribed in the embodiments or the examples of the present invention.

1. An information system of obtaining a combination of part attributevalues as a candidate of a cause of failure from data of a bill ofmaterials holding ID of parts and attribute values of parts by acomputer, comprising: a generation unit of the degree of support forsearching the bill of materials by the ID of parts used for failureproducts; acquiring the attribute values of parts of the searched parts;comparing between the acquired attribute values of parts from eachother; and generating the degree of support to be counted in the case ofthe same values; a part attribute value extracting unit for storingparts having attribute values of parts, whose generated degree ofsupport is higher than a predetermined value and the attribute valuesthereof as frequently-appearing part attribute; a support part combiningunit for preparing a combination of parts having the storedfrequently-appearing part attribute; a common part unit for obtaining acommon part where each part relating to the prepared combination iscommonly set, regarding the bill of materials; a failure entropy amongcommon parts for calculating a failure entropy among common parts, whichis an entropy of parts used for the failed products among the entireobtained common parts; a failure entropy among support parts forcalculating a failure entropy among support parts, which is an entropyof parts used for the failed parts in the common part using the partshaving the frequently-appearing part attribute; an information gaincalculating unit for calculating an information gain, which is adifference between the failure entropy among the common product and thefailure entropy among the support parts; and a selecting unit forselecting a combination of parts having the frequently-appearing partattribute in the case that the calculated information gain is greaterthan a predetermined threshold value.
 2. The information systemaccording to claim 1, wherein the part attribute value extracting unitcombines a plurality of attribute values, and regards as one value andstores as the frequently appearing part attribute.
 3. The informationsystem according to claim 1, wherein in the common part unit, a commonpart where the prepared combination is commonly set is obtained from abill of materials, where a bill of materials having a tree structure ofdata structure where products and parts comprising said products arehierarchically linked are dispersed and stored based upon networkaddress per node.
 4. The information system according to claim 3,wherein in the common part unit, one part relating to the preparedcombination situated at one child node in the part database and data ofparent part situated at a common parent node with another part relatingto the prepared combination, situated at another child node are obtainedbased upon the network address.
 5. The information system according toclaim 1, wherein in the selecting unit, the threshold value is increasedlittle by little and the prepared combination is narrowed down.
 6. Acomputer program product of obtaining a combination of part attributevalues as a candidate of a cause of failure from data of a bill ofmaterials holding ID of parts and attribute values of parts by acomputer, causing a computer to execute the steps of: searching the billof materials by the ID of parts used for failure products; acquiring theattribute values of parts of the searched parts; comparing between theacquired attribute values of parts from each other; and generating thedegree of support to be counted in the case of the same values; storingparts having attribute values of parts, whose generated degree ofsupport is higher than a predetermined value and the attribute valuesthereof as frequently-appearing part attribute; preparing a combinationof parts having the stored frequently-appearing part attribute; commonparts for obtaining a common part where each part relating to theprepared combination is commonly set, regarding the bill of materials;calculating a failure entropy among common parts, which is an entropy ofparts used for the failed products among the entire obtained commonparts; calculating a failure entropy among support parts, which is anentropy of parts used for the failed parts in the common part using theparts having the frequently-appearing part attribute; calculating aninformation gain, which is a difference between the failure entropyamong the common product and the failure entropy among the supportparts; and selecting a combination of parts having thefrequently-appearing part attribute in the case that the calculatedinformation gain is greater than a predetermined threshold value.
 7. Thecomputer program product according to claim 6, wherein for the partattribute values, a plurality of attribute values of parts are combinedand regarded as one value.
 8. The computer program product according toclaim 6, wherein in the step of the common parts, a common part wherethe prepared combination is commonly set is obtained from a bill ofmaterials, where a bill of materials having a tree structure of datastructure where products and parts comprising said products arehierarchically linked are dispersed and stored based upon networkaddress per node.
 9. The computer program product according to claim 8,wherein in the step of the common parts, one part relating to theprepared combination situated at one child node in the part database anddata of parent part situated at a common parent node with another partrelating to the prepared combination, situated at another child node areobtained based upon the network address.
 10. The computer programproduct according to claim 6, wherein in the step of selecting, thethreshold value is increased little by little and the preparedcombination is narrowed down.